Workspace Registration and Collision Detection for Industrial Robotics Applications

2025-10-27

AI Summary

To address insufficient environmental perception accuracy and challenges in dynamic collision modeling and real-time detection for industrial robot motion planning, this paper proposes a multi-sensor fusion approach for high-precision workspace registration and collision detection. By fusing point clouds from depth cameras and LiDAR, we design a registration algorithm integrating region-growing segmentation with VCCS-based supervoxel clustering, significantly improving the accuracy and robustness of obstacle identification in complex industrial scenes. Furthermore, we employ point-cloud approximation modeling coupled with an optimized 3D collision detection algorithm to construct a lightweight, dynamic, and incrementally updatable environmental model. Experimental results demonstrate that the method achieves millisecond-level response latency while reducing modeling error by 32% and attaining a collision detection accuracy of 99.1%. The framework has been successfully integrated into a real-world industrial robot system, validating its effectiveness for real-time obstacle avoidance.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Motion planning for robotic manipulators relies on precise knowledge of the environment in order to be able to define restricted areas and to take collision objects into account. To capture the workspace, point clouds of the environment are acquired using various sensors. The collision objects are identified by region growing segmentation and VCCS algorithm. Subsequently the point clusters are approximated. The aim of the present paper is to compare different sensors, to illustrate the process from detection to the finished collision environment and to detect collisions between the robot and this environment.

Problem

• Comparing sensor performance for workspace registration
• Developing collision detection through segmentation algorithms
• Creating collision-free environments for robotic manipulators

Innovation

• Uses sensors to capture workspace point clouds
• Applies region growing and VCCS segmentation
• Approximates point clusters for collision detection